To train neurosurgery internsincrease their confidencesurgical skills, we employed 3D printing, mold design,casting technology to develop medical simulators. The simulators allow students to adjust the angle of the head based on the location of the angioma (brain tumor), perform a craniotomy, move the soft tissue of the brain aside using forceps, locate the angioma (brain tumor),clip the angioma (remove the brain tumor) using medical instruments,complete the entire training while artificial blood is flowing through the simulator.

This product is a novel risk assessment tool for carotid artery stenosis and stroke. This is a revolutionary healthcare technology using motion analysis and quantification to extract information from pulses for risk assessments. The entire process is completed by taking a short video clip aimed at the neck with only one simple click on any mobile devices or our apparatus, anywhere, anytime. In less than five minutes, the user receives an evaluation report indicating low to high stroke risk. Our product accuracy stands higher than 90% when compared to the clinical outcome. The future indications of this product can be extended to arrhythmia, venous fistula obstruction, etc. This product has the great potential to achieve our dream of “personalized mobile hospital” in the future world.

The AR-assisted endoscopic navigation system for brain surgery combines CT/MRI images, 3D cerebrovascular/nerve models,endoscopic images for surgical planningnavigation. AR glasses can display the 3D navigation inside the patient＇s skull, providing surgeons with intuitive 3D surgical navigation. In addition, the cerebrovascular/nerve model is also superimposed on the endoscopic image, allowing the surgeon to predict the upcoming surgical situation. The AR display endoscopic images can be simultaneously transmitted to the remote site for assistance through 5G communication.

Migraine patients account for about 640 million people (15) in the world,about 1.75 million (9.1) in Taiwan. Our team had developed a wearable brain-computer interface system with the newly developed graphene dry electrodeadaptive noise subspace reconstruction technology. This system combined the physiological indicators before migraine attacksuses AI technology to build an intelligent migraine early warning system,combined BrainSTIM electrical stimulation technology to assist migraine patients.

Our brain waves consist of a lot of nonlinear characteristics. Holo-Hilbert cross-frequency phase clustering (HHCFPC) is a cross-frequency coupling connectivity analysis based on the nonlinear Holo-Hilbert Spectral Analysis (HHSA) method. It is achieved by clustering the vectors of "phase difference" between the phase of a first layer IMF (obtained by HHSA) from one EEG channelthe phase of a second layer IMF from the other EEG channel. The results of HHCFPC can be used as a guide to optimize the parameters (e.g. frequency,frequencydepth of AM) of non-invasive brain stimulations.

Based on our long-term accumulated healthy brain image database, we used meta-analysismultivariate analytical algorithms to extract the cognitive biological characteristics of individual subjects. By using machine learning / artificial intelligence algorithms, these features were further calculated for capturing individual cognition according to related brain networks. This biological index could be a potential marker for evaluating individual brain health, cognitive functions,risk for neurodegenerative diseases.

We have developed a movement assistive system using EEG-controlled functional electric stimulation system. Users’ movement intentions are recognized by brain computer interface, implemented by deep learning network,the detected users’ intentions are then translated into commands to trigger a functional electric stimulation (FES) device to activate users’ particular movements, such as grasping, hand raising, holding a cup, etc. In our study, we have designed our own wireless dry-electrode EEG systemour own FES system.

We have developed the device recorded the neuronal action potential of multiple mouse brainstheir cerebellum with high sampling rate (30 kHz)multiple channels to accelerate the study of the pathogenesis of neurodegenerative diseases. The team hopes to extend the multi-channel BCI devicebrainwave technology to more neurodegenerative disease biomarker analysis to help pharmaceutical companies to explore the pathogenesis of neurodegenerative diseaseshelp in new drug developmentclinical diagnosis.

The technology aims to develop an AI-based integrated system for emotional detections (anger, sadness, happiness,neutral)multimodal physiological signals (ECG, EEG,PPG),apply to patients with cardiovascular disease. Patients monitor their emotionalphysical statusadminister bio-neuro-feedback to improve their well-being, track disease progression,prevent adverse prognosis.

MPT0G211 is a highly selective HDAC6 inhibitor, with comprehensive IP protection. MPT0G211 is mainly focused on multiple myelomabrain tumor indications. For IND package, we have completed CMC in drug substance, CMC in drug product, ADME,most preclinical toxicology studies, only the 28-days repeated dose toxicology study in dog is left. Once the 28-day repeated dose toxicology study in dog is completed, the IND package will be filed in the United StatesTaiwan in Q2 2022.

This project designs a biomedical theranostic chip for neural disorders. It can not only function as an implantable device for treating the Parkinson＇s disease, depression, dementia, but also monitor physiological signals in wearable devices. For the development of medical device, the NeuLive system based on the microchip can accelerate the pre-clinical data collectionverification.

The Al-based web diagnostic system provides an online assessment tool for diagnosing schizophrenia. The Explainable Deep Neural Network classifier is deployed to analyze gray matterwhite matter to derive diagnostic classification of schizophrenia. The structural brain abnormalities associated with schizophrenia is visualized on the AI-based web diagnostic system at individual level.

PCA-BM includes two models: “Automatic BMs Segmentation AI Model”" Distant Brain Failure Prediction Model". The former one uses C2FNAS (coarse to fine network architecture search) to detect the location, size,number of brain metastases. The latter uses radiomics to extract numerous radiographic featuresemploys machine learning methods such as XGBoost to establish a prognostic model of brain metastases. PCA-BM provides more precision treatment decisions for patientsimproves personalizedaccurate overall stereotactic radiosurgery planning.